Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

摘要

Using Bayesian Model Averaging (BMA), an attempt was made to obtaincalibrated probabilistic numerical forecasts of 2-m temperature overIran. The ensemble employs three limited area models (WRF, MM5 and HRM),with WRF used with five different configurations. Initial and boundaryconditions for MM5 and WRF are obtained from the National Centers forEnvironmental Prediction (NCEP) Global Forecast System (GFS) and for HRM theinitial and boundary conditions come from analysis of Global Model Europe(GME) of the German Weather Service. The resulting ensemble of seven memberswas run for a period of 6 months (from December 2008 to May 2009) over Iran.The 48-h raw ensemble outputs were calibrated using BMA technique for 120days using a 40 days training sample of forecasts and relative verificationdata.The calibrated probabilistic forecasts were assessed using rank histogramand attribute diagrams. Results showed that application of BMA improved thereliability of the raw ensemble. Using the weighted ensemble mean forecastas a deterministic forecast it was found that the deterministic-style BMAforecasts performed usually better than the best member's deterministicforecast.